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Capturing the straw in the wind: do short sellers trade on customer information?

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Abstract

This study investigates whether short sellers trade the stocks of suppliers on customer information. Using the daily short-selling data derived from the Trades and Quotes-Regulation SHO database, we find that short sellers exploit the earnings news of major customers to trade the supplier stocks. Our cross-sectional tests show that short sellers’ trading on customer information is reduced when the suppliers and customers have common analysts or a higher percentage of common transient institutional investors, and is exacerbated for supplier–customer pairs when the supplier is more economically linked with the customer or when the short-sale constraint of the supplier is lower. Further analyses indicate that though short sellers’ trading on customer information is mainly driven by their superior ability to interpret the public information of the customers, we find some evidence that short sellers trade on private information of the customers. This study identifies the intermediary role of short sellers in incorporating customer-specific information into the supplier’s stock price and mitigating the supplier–customer anomaly. It adds to a growing body of studies on information transfer along supply chains.

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Notes

  1. On average, suppliers are substantially smaller than customers and highly dependent on their customers (e.g., Cheung et al. 2020; Fee et al. 2006). Cheung et al. (2020) report that the median value of total assets ($ in million) is $178 for suppliers and $5572 for customers, and the median sales dependence of suppliers on customers is 15% and the median cost dependence of customers on suppliers is 0.2%. Hence, the supplier’s information is likely to have little material value for short sellers to trade customer stock, whereas customer information should be more valuable to investors and more likely to attract the attention of short sellers. Furthermore, current accounting standards in the U.S. (SFAS 14 and SFAS 131) require public firms to disclose major customers (greater than 10% of sales), but not major suppliers. Therefore, we can directly identify a firm’s all major customers but cannot identify a firm’s all major suppliers. SFAS 14 and 39 stipulate that “if 10% or more of the revenue of an enterprise is derived from sales to any single customer, that fact and the amount of revenue from each such customer shall be disclosed.”.

  2. In this study, we only consider transient institutional investors as previous research has generally confirmed that unlike dedicated institutional investors who are long-term active monitors and quasi-indexers who are passive investors, transient institutional investors provide information through frequent trading (Bushee 1998; Yan and Zhang 2009). We follow Bushee (1998) and label institutional investors as transient, dedicated or quasi-indexing institutional investors.

  3. As shown in Sect. 5.4.2, we measure the dependence of supplier on customer based on the percentage of sales to the customer or product complexity proxied by R&D expenditures over total assets. Firms with R&D expenditures over total assets above the year-quarter median in the quarter before the earnings announcement of the customer have high product complexity.

  4. We obtain similar results with various alternative event windows including [0, 5], [− 5, − 1], [0, 3] and [− 3, − 1] days, respectively. Alternatively, we restrict our sample to the QEA of customers in the first year of the customer–supplier relationship, which likely precedes the public disclosure of the relationship to the market (Alldredge and Cicero 2015). We do not find any negative association between the CAVSS and the unexpected earnings of the customers in this subsample.

  5. We also recognize a possibility that short sellers of suppliers don’t exploit the information of customers due to the following reasons. First, short sellers may trade to exploit liquidity rather than information (Von Beschwitz et al. 2017). Second, short sellers may not be attentive to the customer information if they don’t hold or short sell the stocks of customers. Third, even though short sellers are attentive to the information of customers, they may not use it due to high cost of short selling.

  6. The majority of previous research on U.S. supplier–customer relationships in accounting and finance use the same data source (for example, Fee and Thomas 2004; Fee et al. 2006; Cohen and Frazzini 2008; Banerjee, Dasgupta, and Kim 2008; Hui et al. 2012).

  7. Alternatively, we examine the short selling activities in the event window of [− 5, 5] and [− 3, 3] days relative to the QEAs of customers and report results in Tables 3 and 4.

  8. Alternatively, we use the average daily short-sale volume of each supplier firm in our sample excluding observations in the event window of [− 10, 10] days relative to the QEAs of customers to measure normal short-sale volume, and find our main results are qualitatively unchanged.

  9. For tests where short-sale volumes of the supplier stocks are measured in the event window of [− 3, 3], Idiosyncratic risk, Return and Volume are measured in the event window of [− 30, − 4].

  10. We use the permanent identifiers of institutional investors, which are provided by Bushee (1998) and can be retrieved from his website (http://acct.wharton.upenn.edu/faculty/bushee/IIclass.html), to determine whether the institutional investor is the common investor for both the supplier and customer (holds the stocks of both the supplier and customer).

  11. The results in Column 1 of Table 5 are qualitatively unchanged when we control for an indicator, which is 1 if the number of analysts that make earnings forecasts of suppliers is above the full sample median of the fiscal year and 0 otherwise, and its interaction with Customer SUE.

  12. The results in Column 2 of Table 5 are qualitatively unchanged when we control for an indicator, which is 1 if the number of institutional investors of suppliers is above the full sample median of the year-quarter and 0 otherwise, and its interaction with Customer SUE.

  13. Our data of supplier–customer relationships begin in 1976.

  14. Stambaugh et al. (2015) construct the measure of a firm’s stock overpricing based on the following eleven anomalies identified in the literature: financial distress probability, O-score bankruptcy probability, net stock issues, composite equity issues, total accruals, net operating assets, momentum, gross profitability, asset growth, return on assts and investment-to-assets. The measure is the average of the ranking percentile for each of the eleven anomalies. Stocks with highest values of the measure are the most overpriced and those with the lowest values of the measure are the most underpriced. The data is available via Professor Stambaugh’s website: https://finance.wharton.upenn.edu/~stambaug/.

  15. The results of Customer SUE*Overpricing (Customer SUE*Underpricing) do not change if we measure short-sale volume around customer QEAs in the window of [-10, -1] or [0,10] regarding the trading activities on public/private information.

  16. The results are qualitatively unchanged when we control for the level of stock overpricing/underpricing of suppliers.

  17. Every third stock ranked by average daily trading volume during the year before the pilot program was announced in the index are selected (Securities and Exchange Commission 2007).

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Acknowledgements

We thank C.-F. Lee (the Editor) and two anonymous reviewers. We appreciate helpful comments from Danqi Hu (discussant), Sudipta Basu, Rashad Abdel-Khalik, Jeffrey Callen, Tim Simin (discussant), and participants at 2020 AAA virtual Annual Meeting, Multinational Finance Society Conference in Jerusalem and 29th Annual Pacific Basin Finance, Economics, Accounting and Management Conference, and research seminar participants at Central University of Finance and Economics and Hang Seng University of Hong Kong. All coauthors have made equal contribution to the paper. Wenming Wang acknowledges the financial support from the Key Project of Philosophy and Social Sciences Research sponsored by Ministry of Education of China [No. 20JZD014]. Wenlan Zhang acknowledges financial support from the National Natural Science Foundation of China (No. 71702188). The authors alone are responsible for all limitations and errors that may relate to the paper.

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Authors and Affiliations

  1. Neeley School of Business, Texas Christian University, Fort Worth, TX, 76129, USA

    In-Mu Haw

  2. Department of Finance and Accounting, School of Management, Zhejiang University, Hangzhou, 310058, People’s Republic of China

    Wenming Wang

  3. School of Accounting, Dongbei University of Finance and Economics, Dalian, 116025, People’s Republic of China

    Wenlan Zhang

  4. Department of Accounting, Faculty of Business Administration, University of Macau, Av. Padre Tomás Pereira, Taipa, Macau

    Xu Zhang

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  1. In-Mu Haw
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Correspondence to In-Mu Haw.

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Appendix

Appendix

See Table 13.

Table 13 Variable definitions
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Haw, IM., Wang, W., Zhang, W. et al. Capturing the straw in the wind: do short sellers trade on customer information?. Rev Quant Finan Acc 58, 1363–1394 (2022). https://doi.org/10.1007/s11156-021-01027-7

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